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Preventive care services (PCS) extend life, yet physicians often underprioritize highly effective interventions such as lifestyle modifications (Zhang et al., JAMA Network Open 2020). We evaluated whether large language models (LLMs) replicate and augment physician prioritization of PCS under time constraints. Using Zhang et al.'s validated survey with two patients assessed during long and short visits, we compared seven LLMs with historical physicians. We generated 137 simulated physician personas matching cohort demographics and tested three prompts per model. Primary outcomes were concordance with physician rankings, measured by Spearman correlation, and Consensus-Stratified Agreement (CSA), the proportion of LLM selections rated 4 or higher that matched physician consensus across agreement strata. Secondary outcomes included life-years gained per prioritized choice (LYGPC), consistency, and selectiveness. Augmentation was assessed by having models revise physician rankings under three informative prompts, with delta LYGPC quantifying impact. LLMs closely mirrored physicians (mean Spearman = 0.83, SD = 0.11), with high CSA at extreme agreement ranges (94%, 197/210) but low CSA i

Wi-Fi networks increasingly suffer from performance degradation caused by contention-based channel access, dense deployments, and largely self-managed operation among mutually interfering access points (APs). In this paper, we propose a Digital Twin (DT) framework that captures the essential spatial and temporal characteristics of wireless channels and traffic patterns, enabling the prediction of likely future network scenarios while respecting physical constraints. Leveraging this predictive capability, we introduce two analytically derived performance upper bounds-one based on Shannon capacity and the other on latency behavior under CSMA-CA (Carrier Sense Multiple Access with Collision Avoidance)-that can be evaluated efficiently without time-consuming network simulations. By applying importance sampling to DT-generated scenarios, potentially risky network conditions can be identified within large stochastic scenario spaces. These same performance bounds are then used to proactively guide a gradient-based search for improved network configurations, with the objective of avoiding imminent performance degradation rather than pursuing globally optimal but fragile solutions. Simulati

In this work, a single-unit multi-state system is considered. The system is subject to internal failures, as well as external shocks with multiple consequences. It also incorporates a preventive maintenance strategy and a Bernoulli vacation policy for the repairperson. It is algorithmically modeled in both continuous and discrete time using Marked Markovian Arrival Processes (MMAP). The system's operation/degradation level is divided into an indeterminate number of levels. Upon returning from a vacation period, the repair technician may initiate corrective repair, perform preventive maintenance, replace the unit, remain idle at the workplace, or begin a new vacation period. The decision in the latter two cases is made probabilistically based on the system's operational level. This methodology allows the model and its associated measures to be algorithmically derived in both transient and stationary regimes, presented in a matrix-algorithmic form. Analytical-matrix methods are used to obtain the system's steady-state behaviour as well as various performance measures. Costs and rewards are introduced to analyze when the system becomes profitable. Measures associated with costs over t

Chronic diseases are long-term, manageable, yet typically incurable conditions, highlighting the need for effective preventive strategies. Machine learning has been widely used to assess individual risk for chronic diseases. However, many models rely on medical test data (e.g. blood results, glucose levels), which limits their utility for proactive self-assessment. Additionally, to gain public trust, machine learning models should be explainable and transparent. Although some research on self-assessment machine learning models includes explainability, their explanations are not validated against established medical literature, reducing confidence in their reliability. To address these issues, we develop deep learning models that predict the risk of developing 13 chronic diseases using only personal and lifestyle factors, enabling accessible, self-directed preventive care. Importantly, we use SHAP-based explainability to identify the most influential model features and validate them against established medical literature. Our results show a strong alignment between the models' most influential features and established medical literature, reinforcing the models' trustworthiness. Crit

Cyber attacks continue to be a cause of concern despite advances in cyber defense techniques. Although cyber attacks cannot be fully prevented, standard decision-making frameworks typically focus on how to prevent them from succeeding, without considering the cost of cleaning up the damages incurred by successful attacks. This motivates us to investigate a new resource allocation problem formulated in this paper: The defender must decide how to split its investment between preventive defenses, which aim to harden nodes from attacks, and reactive defenses, which aim to quickly clean up the compromised nodes. This encounters a challenge imposed by the uncertainty associated with the observation, or sensor signal, whether a node is truly compromised or not; this uncertainty is real because attack detectors are not perfect. We investigate how the quality of sensor signals impacts the defender's strategic investment in the two types of defense, and ultimately the level of security that can be achieved. In particular, we show that the optimal investment in preventive resources increases, and thus reactive resource investment decreases, with higher sensor quality. We also show that the de

Preventive control is a crucial strategy for power system operation against impending natural hazards, and its effectiveness fundamentally relies on the realism of scenario generation. While most existing studies employ sequential Monte Carlo simulation and assume independent sampling of component failures, this oversimplification neglects the spatial correlations induced by meteorological factors such as hurricanes. In this paper, we identify and address the gap in modeling spatial dependence among component failures under extreme weather. We analyze how the mean, variance, and correlation structure of weather intensity random variables influence the correlation of component failures. To fill this gap, we propose a spatially dependent sampling method that enables joint sampling of multiple component failures by generating correlated meteorological intensity random variables. Comparative studies show that our approach captures long-tailed scenarios and reveals more extreme events than conventional methods. Furthermore, we evaluate the impact of scenario selection on preventive control performance. Our key findings are: (1) Strong spatial correlations in uncertain weather intensity

Education plays a critical role on promoting preventive behaviours against the spread of pandemics. In Japan, hand-washing education in primary schools was positively correlated with preventive behaviours against COVID-19 transmission for adults in 2020 during the early stages of COVID-19 [1]. The following year, the Tokyo Olympics were held in Japan, and a state of emergency was declared several times. Public perceptions of and risks associated with the pandemic changed drastically with the emergence of COVID-19 vaccines. We re-examine whether effect of hand-washing education on preventive behaviours persisted by covering a longer period of the COVID-19 pandemic than previous studies. 26 surveys were conducted nearly once a month for 30 months from March 2020 (the early stage of COVID-19) to September 2022 in Japan. By corresponding with the same individuals across surveys, we comprehensively gathered data on preventive behaviours during this period. In addition, we asked about hand-washing education they had received in their primary school. We used the data to investigate how and the degree to which school education is associated with pandemic mitigating preventive behaviours. W

Transient stability-constrained preventive redispatch plays a crucial role in ensuring power system security and stability. Since redispatch strategies need to simultaneously satisfy complex transient constraints and the economic need, model-based formulation and optimization become extremely challenging. In addition, the increasing uncertainty and variability introduced by renewable sources start to drive the system stability consideration from deterministic to probabilistic, which further exaggerates the complexity. In this paper, a Graph neural network guided Distributional Deep Reinforcement Learning (GD2RL) method is proposed, for the first time, to solve the uncertainty-aware transient stability-constrained preventive redispatch problem. First, a graph neural network-based transient simulator is trained by supervised learning to efficiently generate post-contingency rotor angle curves with the steady-state and contingency as inputs, which serves as a feature extractor for operating states and a surrogate time-domain simulator during the environment interaction for reinforcement learning. Distributional deep reinforcement learning with explicit uncertainty distribution of syst

A complex multi-state redundant system undergoing preventive maintenance and experiencing multiple events is being considered in a continuous time frame. The online unit is susceptible to various types of failures, both internal and external in nature, with multiple degradation levels present, both internally and externally. Random inspections are continuously monitoring these degradation levels, and if they reach a critical state, the unit is directed to a repair facility for preventive maintenance. The repair facility is managed by a single repairperson, who follows a multiple vacation policy dependent on the operational status of the units. The repairperson is responsible for two primary tasks: corrective repairs and preventive maintenance. The time durations within the system follow phase-type distributions, and the model is constructed using Markovian Arrival Processes with marked arrivals. A variety of performance measures, including transient and stationary distributions, are calculated using matrix-analytic methods. This approach enables the expression of key results and overall system behaviour in a matrix-algorithmic format. In order to optimize the model, costs and rewar

This manuscript studies the preventive replacement policy for a series or parallel system consisting of n independent or dependent heterogeneous components. Firstly, for the age replacement policy, Some sufficient conditions for the existence and uniqueness of the optimal replacement time for both the series and parallel systems are provided. By introducing deviation costs, the expected cost rate of the system is optimized, and the optimal replacement time of the system is extended. Secondly, the periodic replacement policy for series and parallel systems is considered in the dependent case, and a sufficient condition for the existence and uniqueness of the optimal number of periods is provided. Some numerical examples are given to illustrate and discuss the above preventive replacement policies.

False data injection (FDI) cyber-attacks on power systems can be prevented by strategically selecting and protecting a sufficiently large measurement subset, which, however, requires adequate cyber-defense resources for measurement protection. With any given cyber-defense resource, this paper proposes a preventive-corrective cyber-defense strategy, which minimizes the FDI attack-induced region in a preventive manner, followed by maximizing the cybersecurity margin in a corrective manner. First, this paper proposes a preventive cyber-defense strategy that minimizes the volume of the FDI attack-induced region via preventive allocation of any given measurement protection resource. Particularly, a sufficient condition for constructing the FDI unattackable lines is proposed, indicating that the FDI cyber-attack could be locally rather than globally prevented. Then, given a non-empty FDI attack-induced region, this paper proposes a corrective cyber-defense strategy that maximizes the cybersecurity margin, leading to a trade-off between the safest-but-expensive operation point (i.e., Euclidean Chebyshev center) and the cheapest-but-dangerous operation point. Simulation results on a modifi

This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical savings.

Dairy farming has great economic value in Brazil, however, during production, diseases such as mastitis can occur in animals, which can reduce productivity and, consequently, economic profitability. When mastitis is present in animals, it can cause physical and chemical changes in the milk, affecting its quality, market value and also compromising the health of the animal. MastiteApp is a tool to help producers prevent mastitis in their herds by checking the temperature taken from the four teats of the animal. To perform theanalysis, the temperature of all the animals' teats must be measured and, if there is a change in temperature, the system will display a message informing the producer of the possible presence of subclinical mastitis in their animal. The application has proven to be efficient in alerting producers to the possible presence of subclinical mastitis in the first few days of manifestation, thus initiating treatment and preventing the disease from worsening.

With the increase in data volume, more types of data are being used and shared, especially in the power Internet of Things (IoT). However, the processes of data sharing may lead to unexpected information leakage because of the ubiquitous relevance among the different data, thus it is necessary for data owners to conduct preventive audits for data applications before data sharing to avoid the risk of key information leakage. Considering that the same data may play completely different roles in different application scenarios, data owners should know the expected data applications of the data buyers in advance and provide modified data that are less relevant to the private information of the data owners and more relevant to the nonprivate information that the data buyers need. In this paper, data sharing in the power IoT is regarded as the background, and the mutual information of the data and their implicit information is selected as the data feature parameter to indicate the relevance between the data and their implicit information or the ability to infer the implicit information from the data. Therefore, preventive audits should be conducted based on changes in the data feature pa

Problem Definition. Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Methodology/Results. In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk. Specifically, we combine counterfactual inference, machine learning, and optimization techniques to build a scalable decision model that can exploit high-dimensional medical data, such as the data found in modern electronic health records. Our decision model is evaluated based on electronic health records from 89,191 prediabetic patients. We compare the allocation of preventive treatments (metformin) prescribed by our data-driven decision model with that of current practice. We find that if our approach is applied to the U.S. population, it can yield annual savings of $1.1 billion. Finally, we analyze the cost-effectiveness under varying budget levels. Managerial Implications. Our work supports decision-making in health management, with the goal of achieving effective disease prevention at lower costs. Importantly, our decision model is generic

This paper presents a reliability life analysis and preventive maintenance schedule for ducted wind turbines. Ducted wind turbines (DWT) are an emerging segment of the renewable energy industry with innovations that promise reliable, efficient, low-cost energy for consumer and small business markets. Many attempts have been made to build viable ducted turbines over the last century, but until recently none have succeeded commercially. Optimal shroud and blade designs are the focus of most engineering research to improve performance and efficiency, however, we hypothesize that an equally important key to the long-term success of small wind innovations is reliability analysis. For consumers and companies who want to efficiently maximize the lifespan of DWTs, this has significant ramifications. Operating beyond service life can result in catastrophic component failure and high replacement costs, making the technology economically infeasible. Our approach is focused on the analysis of 3.5 kW D3 turbines manufactured by Ducted Wind Turbines, Inc. We develop a component-level reliability analysis using ASTM E3159 and a consumer-level preventative maintenance schedule including failure mo

The article constructs a game-theoretic model of preventive measures for the antagonistic game with nature, that is such a model in which two participants take part, one of which is a regulating body (government) that makes legislative decisions depending on the emerging external and internal conditions affecting the course of the demographic process, the second participant - internal and external conditions of the environment (nature). An example of such a game is given and the optimal strategy for the government is calculated.

Existing machine learning-based surrogate modeling methods for transient stability constrained-optimal power flow (TSC-OPF) lack certifications in the presence of unseen disturbances or uncertainties. This may lead to divergence of TSC-OPF or insecure control strategies. This paper proposes a neural network certification-informed power system transient stability preventive control method considering the impacts of various uncertainty resources, such as errors from measurements, fluctuations in renewable energy sources (RESs) and loads, etc. A deep belief network (DBN) is trained to estimate the transient stability, replacing the time-consuming time-domain simulation-based calculations. Then, DBN is embedded into the iterations of the primal-dual interior-point method to solve TSC-OPF. To guarantee the robustness of the solutions, the neural network verifier $α, β$-CROWN to deal with uncertainties from RESs and loads is proposed. The yielded certification results allow us to further adjust the transient stability safety margin under the iterated TSC-OPF solution process, balancing system security and economics. Numerical results on a modified western South Carolina 500-bus system de